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AI 模型 2119
- wide_resnet50_2.tv_in1k↗
timm
This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 68.9 - GMACs: 11.4 - Activations (M): 14.4 - Image size: 224 x 224 - **Papers:** - Wide Residual Networks: https://arxiv.org/abs/1605.07146 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/pytorch/vision Explore the dataset and runtime metrics of this model in timm model results.
- Samantha-Qwen-2-7B-GGUF↗
bartowski
- Qwen3.6-35B-A3B-GGUF↗
byteshape
This is a GGUF-quantized version of Qwen3.6-35B-A3B produced with **ByteShape's ShapeLearn**, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths. To learn more about ShapeLearn and to see detailed benchmarks across GPUs, CPUs, and even the Raspberry Pi, please visit our blog. Pick a model from the tables below and click **Get llama.cpp command** to get a ready-to-run command with all the correct sampling parameters for this model.
- Ternary-Bonsai-2-27B-Abliterated-swift-cap-PQ2_0-MTP↗
hotdogs
**BoldingBuilds' abliterated Bonsai 2 v2, with two SVD-LoRAs merged in at 0.4 each:** `hotdogs/swift_unc_qwen3.8-27B_lora` (abliteration + Swift-1.5 reasoning-efficiency) and `hotdogs/thinkingcap_qwen3.8-27B_svd_lora` (ThinkingCap token-efficient reasoning). Same 27B ternary runtime as the base — **PQ2_0 packing, Hadamard latent, MTP head, 262,144-token context**, ~7.6 GB on disk. Vision projector included. > ⚠️ **This file needs PrismML's llama.cpp fork.** PQ2_0 does **not** load in mainline llama.cpp, Ollama, LM Studio or llama.app. See Requirements.
- stack-edu-classifier-javascript↗
HuggingFaceTB
This is a classifier for scoring the educational value of code files in The Stack v2 dataset, it is a finetuned version of bigcode/starencoder with a classification head on code files annotated by Llama3.1-70B-Instruct. We use this classifier for building Stack-Edu dataset used for training SmolLM2, see paper. Each classifier is trained on one programming language. While the classifier performs well in distinguishing high-quality code in its target language (JS in this case), there are some limitations: - Scope: The model's performance might change for other datasets, in particular for out of distribution samples. The classifier's context is 1024 tokens, which might not be sufficient to assess the quality of some long code files. - Bias: The model's performance is dependent on the quality and representativeness of the training data and the LLM used for the annotation. Biases in both can affect the classifier's judgments. It might overfit to thoroughly commented code. - Context: The classifier evaluates individual code files without considering broader context, which might impact its effectiveness in certain scenarios.
- Qwen3.5-122B-A10B-abliterix-NVFP4↗
bjk110
NVFP4 (W4A4) quantization of `wangzhang/Qwen3.5-122B-A10B-abliterix` — the 122B/A10B Abliterix-abliterated (uncensored) Qwen3.5 MoE — packaged for vLLM serving on NVIDIA Blackwell hardware (DGX Spark GB10 / SM121). The Abliterix-trained uncensored behavior (0.5% refusal rate, KL divergence 0.0115 vs the Qwen3.5-122B-A10B baseline) is preserved while dropping weight memory from BF16 (~230 GB) to NVFP4 (~70 GB) — fitting comfortably on two DGX Spark nodes (2 × 119 GiB unified memory) with headroom for KV-cache, with the same compressed-tensors runtime path as the FP8 sibling. Compared to the FP8 W8A8 variant (bjk110/Qwen3.5-122B-A10B-abliterix-FP8, 116 GB), this NVFP4 build cuts another ~40% off-disk and unlocks the **FlashInfer-CUTLASS NVFP4 MoE backend** for higher throughput on Blackwell.
- gliclass-base-v3.0↗
knowledgator
This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path. It can be used for `topic classification`, `sentiment analysis`, and as a reranker in `RAG` pipelines. The model was trained on logical tasks to induce reasoning. LoRa adapters were used to fine-tune the model without destroying the previous knowledge.
- stable-diffusion-xl-base-1.0↗
Stability AI
SDXL 由一个用于潜在扩散的专家集成管道组成：第一步，使用基础模型生成（带噪声的）潜在变量，随后利用专门用于最终去噪步骤的精炼模型对其进行进一步处理（可在此处获取：https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/）。请注意，基础模型可用作独立模块。或者，我们也可以采用如下两阶段管道：首先，使用基础模型生成所需输出尺寸的潜在变量。第二步，使用专用的高分辨率模型，并对第一步生成的潜在变量应用一种称为 SDEdit 的技术（https://arxiv.org/abs/2108.01073,，也称为“img2img”），同时使用相同的提示词。由于该技术需要更多的函数评估，因此速度略慢于第一种方法。出于研究目的，我们推荐访问我们的 `generative-models` GitHub 仓库（https://github.com/Stability-AI/generative-models），其中实现了最流行的扩散框架（包括训练和推理），并且未来还将陆续添加蒸馏等新功能。Clipdrop 提供免费的 SDXL 推理服务。
- flux-3-image-klein↗
unsloth
> Ungated mirror of black-forest-labs/flux-3-image-klein, republished by Unsloth so it > can be downloaded without a Hub gate. The weights are unmodified. See NOTICE.md and LICENSE.md > in this repo for the terms, which are the upstream terms and are unchanged. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. This repository holds the distilled checkpoint for generation and image editing. For fine-tuning, LoRA training and research, use FLUX 3 Image Klein Base.
- WizardMath-7B-V1.1↗
WizardLMTeam
Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on GSM8k and MATH test set. Please use **the same systems prompts strictly** with us, and we do not guarantee the accuracy of the **quantified versions**.